Abstract MP49: Sugar Sweetened and Artificially Sweetened Beverages and risk of Mortality in US adults
Bibliographic record
Abstract
Background: Sugar sweetened beverages (SSBs) are the single largest source of calories and added sugars in the US diet and regular consumption has been associated with weight gain and risk of chronic diseases. Artificially sweetened beverages (ASBs) are often suggested as alternatives to SSB but little is known about their long-term health effects. Whether consumption of SSBs or ASBs is associated with risk of mortality is unknown. Methods: We prospectively followed 38,602 men from the Health Professional’s Follow-up study (1986-2010) and 82,592 women from the Nurses’ Health study (1980-2010) who were free from cardiovascular disease (CVD) and cancer at baseline. Diet was assessed using validated food frequency questionnaires every 4 years and Cox Proportional Hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI). Results: We documented 27,691 deaths (6,631 CVD and 10,447 cancer deaths) during 3.14 million person-years. After adjusting for major dietary and lifestyle risk factors, and BMI, baseline diabetes, hypertension and hypercholesterolemia, consumption of SSBs was associated with an increased risk of total mortality, which was mainly driven by CVD mortality among individuals consuming at least 2 servings per day; pooled HRs (95% CIs) across categories (<1/month, 1-4/month, 2-6/week, 1-<2/day and ≥2/day) were 1.00, 0.95 (0.91, 0.98), 0.96 (0.93, 0.99), 1.02 (0.96, 1.08), and 1.18 (1.04, 1.33), respectively (P-trend= 0.0001) for total mortality, and 1.00, 0.97 (0.90, 1.02), 0.96 (0.90, 1.02), 1.04 (0.93, 1.16) and 1.28 (1.09, 1.51), respectively (P-trend=0.007) for CVD mortality. In contrast, ASBs were not associated with mortality; pooled HR’s (95% CIs) across categories (<1/month, 1-4/month, 2-6/week, 1-<2/day and ≥2/day) were 1.00, 0.92 (0.89, 0.95), 0.91 (0.86, 0.97), 0.91 (0.86, 0.95) and 0.99 (0.85, 1.15), respectively (P-trend=0.50) for total mortality and 1.00, 0.86 (0.80, 0.92), 0.87 (0.81, 0.94), 0.96 (0.88, 1.06) and 0.96 (0.74, 1.25), respectively (P-trend=0.99) for CVD mortality. No associations were observed with cancer mortality for either SSBs or ASBs in multi-variable adjusted models. Conclusion: Regular consumption of SSBs is associated with an increased risk of total and CVD mortality, providing additional support for recommendations and policies to limit intake of these beverages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".